Conversational explainability

ABSTRACT

Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to provide explanations in response to user-submitted questions. A user query may be received and a classification (e.g., a general question, a specific question) for the user query may be determined based at least in part on a predefined rule set (or a classification model). A set of decision features (e.g., particular user data) associated with a decision generated by a machine-learning model may be identified. An explanation chain may be identified from a plurality of explanation chains based at least in part on the user query. The explanation chain may describe a logical chain of explanations associated with a decision making process related to the machine-learning model. A response to the user query may be provided based at least in part on the explanation chain and the set of decision features.

CROSS-REFERENCE TO RELATED APPLICATIONS

This non-provisional application claims priority to U.S. PatentApplication No. 62/985,666, filed on Mar. 5, 2020, the disclosure ofwhich is herein incorporated by reference in its entirety for allpurposes.

TECHNICAL FIELD

This disclosure is generally concerned with linguistics. Morespecifically, this disclosure relates to using providing answers thatinclude explanations to user submitted questions.

BACKGROUND

Linguistics is the scientific study of language. One aspect oflinguistics is the application of computer science to human naturallanguages such as English. Due to the greatly increased speed ofprocessors and capacity of memory, computer applications of linguisticsare on the rise. For example, computer-enabled analysis of languagediscourse facilitates numerous applications such as automated agentsthat can answer questions from users. The use of “chatbots” and agentsto answer questions, facilitate discussion, manage dialogues, andprovide social promotion is increasingly popular. To address this need,a broad range of technologies including compositional semantics has beendeveloped. Such technologies can support automated agents in the case ofsimple, short queries and replies.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an autonomous agent environment, in accordance with atleast one embodiment.

FIG. 2 depicts an example complete discourse tree of an exampleparagraph, in accordance with at least one embodiment.

FIG. 3 depicts an example semantic parse of the example paragraphcorresponding to FIG. 2 , in accordance with at least one embodiment.

FIG. 4A depicts an example discourse tree from which an explanationchain may be generated, in accordance with at least one embodiment.

FIG. 4B depicts an example complete discourse tree corresponding to thediscourse tree of FIG. 4A, in accordance with at least one embodiment.

FIG. 5 illustrates an exemplary method for generating augmented trainingdata for a machine learning model, in accordance with at least oneembodiment.

FIG. 6 depicts a schematic diagram of computing components of a questionanswering engine, in accordance with at least one embodiment.

FIG. 7 depicts a flowchart illustrating an example of a process fordiscriminating between questions and a request in accordance with atleast one embodiment.

FIG. 8 depicts a flowchart illustrating examples of rules used fordiscriminating between a question and a request, in accordance with atleast one embodiment.

FIG. 9 depicts a flowchart of an exemplary process for training amachine learning model to detect whether an utterance is a question or arequest, in accordance with at least one embodiment.

FIG. 10 depicts a flow diagram of an example flow for providingconversational explanations, in accordance with at least one embodiment.

FIG. 11 depicts a flowchart illustrating an example of a method forproviding conversational explanations, in accordance with at least oneembodiment.

FIG. 12 depicts a simplified diagram of a distributed system forimplementing one of the aspects.

FIG. 13 is a simplified block diagram of components of a systemenvironment by which services provided by the components of an aspectsystem may be offered as cloud services in accordance with an aspect.

FIG. 14 illustrates an exemplary computer system, in which variousaspects may be implemented.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to providing conversationalexplanations. Machine learning has great potential for improvingproducts, processes and research. But computers usually do not explaintheir predictions which can be a barrier to the adoption of machinelearning. Despite widespread adoption, machine learning (ML) modelsremain mostly black boxes. Understanding the reasons behind predictionsmay be important in assessing trust, if one plans to take action basedon a prediction, or when choosing whether to deploy a new model. Suchunderstanding also provides insights into the model, which can be usedto transform an untrustworthy model or prediction into a trustworthyone. Whether humans are directly using machine learning classifiers astools, or are deploying models within other products, a vital concernremains: if the users do not trust a model or a prediction, they willnot use it.

It is important to distinguish two different ML-components of trust: 1)trusting a prediction, i.e. whether a user trusts an individualprediction sufficiently to take some action based on it; and 2) trustinga model, i.e. whether the user trusts a model to behave in reasonableways if deployed. Both are directly impacted by how much the humanunderstands a model's behavior, as opposed to seeing it as a black box.

In some embodiments, an explanation of a decision of an abstract MLsystem may be provided in the form of a conversation, to improve theuser's trust in that this decision is fair and reasonable, even if thisdecision does not favor a goal of this user. One value of aconversational explanation versus a static explanation in the form of areport is that it can provide as much or as little of the availabledetails as the user desires, without overloading a user with detailsoutside of her interest. Another value of conversation instead ofsingle-step search is a user capability of drilling in and/or drillingout of factors determined to be essential when the decision was made.Therefore, an explanation in a conversational form can be an efficientway to increase users' trust of both particular decision and ML model.

In some embodiments, a conversational interface may be utilized for adecision log of an abstract ML system that enumerates the decision stepsand features employed to arrive to a given decision. Conversationalcontent may augment the decision log with background knowledge to betterhandle questions and provide complete answers. As a result, the userswho can chat with the decision making system may develop a substantiallyhigher trust in it than they would with a black-box ML or conventionalreport-based explanation. Conversational explainability (CE) (e.g., aterm for utilizing providing explanations in the conversationalinterfaces discussed herein) may allow the user(s) to get into as manydetails as they wish concerning the decision process. Conversationalexplanation may be considered a special form of dialogue management thatfollows an explanation chain (discussed in more detail below). Theproposed CE system delivers meaningful explanation in 65% cases, whereasa conventional, report-based explanations—in 49% of cases (for the samedecision sessions).

If a ML model performs well, why do not we just trust the model andignore why it made a certain decision? One problem is that a singlemetric, such as classification accuracy, is an incomplete description ofmost real-world ML tasks. Therefore beyond supporting a trust by a user,explainability can be a measure of an ML system's own performanceirrespectively of its interpretation by an observer.

The conversational interface discussed herein may utilize free-formnatural language (NL) explanations, instead of providing explanations ina formal language. The utilization of NL explanations may provide anumber of benefits. For example, NL is readily comprehensible to anend-user who needs to assert a model's reliability. NL may be theeasiest for humans to provide free-form language, which may eliminatethe additional effort of learning to produce formal language, thusmaking it simpler to collect such datasets. NL justifications mighteventually be mined from existing large-scale free-form text.

Consider the domain of personal finance, where customers are applyingfor a loan from a financial institution such as a bank. It can beassumed that the bank has a complex existing process based on acombination of data-driven approaches and business rules to arrive at afinal loan approval decision. A loan officer has an option to validatethe approval or denial recommendations of an AI model. The applyingcustomer may be interested in the reasoning behind the decision, inparticular, when a loan application is denied. They may want to knowabout a few factors that could be changed to improve their profile forpossible approval in the future. Also, a data science executive may needsome assurance that in most cases the recommendations made by the modelare reasonable.

As a non-limiting example, a bank customer may want answers for thefollowing questions:

-   -   Q1: Why was my application rejected?    -   Q2: What can I improve to increase the likelihood my application        is accepted?        Providing an explanation will help them understand if they've        been treated fairly, and also provide insight into what—if their        application was rejected—they can improve in order to increase        the likelihood it will be accepted in the future. An        explainability algorithm may be utilized with a ML component to        help detect both the features that a bank customer could improve        (e.g., amount of time since last credit inquiry, average age of        accounts), and also further detects the features that will        increase the likelihood of approval and those that are within        reach for the customer.

In some embodiments, a static report may be provided that indicates athe factor(s) that contributed to the decision. By way of example, areport may indicate that the customer's consolidated risk markers valueis 33, but that it should be closer to 40 for the application to beapproved. Additionally, the report may indicate that although thecustomer's average age of accounts was 43 months, the average age shouldbe closer to 55 months in order to for his application to be approved.The report may also indicate that the customer's last credit inquiry wasthree months ago and it may need to be five or more months for theapplication to be approved. In some embodiments, rather than a staticreport, the decision information discussed above may be provided inconversational format, an example of which is provided below.

-   -   Question: Is my mortgage application approved    -   Answer: No    -   Question: Why was my mortgage application denied?”    -   Answer: The primary reason your application was denied is that        your consolidated risk markers score is low.    -   Question: What can I do about it?    -   Answer: You should pay down the balances of your credit cards.    -   Question: What are the other reasons my mortgage application was        denied?    -   Answer: Another reason is that the average age of your accounts        is low.    -   Question: How do I avoid this?    -   Answer: There's not much you can do now except wait for your        accounts to age.

The disclosed techniques are directed to generating explanations inresponse to a user query.

FIG. 1 depicts autonomous agent environment 100, in accordance with atleast one embodiment.

FIG. 1 depicts computing device 102, data network 104, and user device106. Computing device 102 includes one or more of autonomous agentapplication 108 which in turn may include a question answering engine112. The computing device 102 may further include database 114, trainingdata 116, database 118, classifier 120, machine-learning model 122, andexplanation chain manager 124. User device 106 may include userinterface 130.

User device 106 can be any mobile device such as a mobile phone, smartphone, tablet, laptop, smart watch, and the like. User device 106communicates via data network 104 to computing device 102. Data network104 can be any public or private network, wired or wireless network,Wide Area Network, Local Area Network, or the Internet.

In some embodiments, explanation chain manager 124 may be configured toutilize a corpus of documents stored in database 114 (e.g., documentsobtained from searching the Internet or intranet or other publically orprivately available data sources) to generate a number of explanationchains. An explanation within text S (e.g., a document obtained from theInternet related to mortgage loan applications) may include a chain ofpremises P₁, . . . , P_(m) which imply S. The chain of P₁, . . . , P_(m)may be referred to as “an explanation chain.” S may be referred to as asubject of the explanation. For this explanation chain P₁, . . . , P_(m)each element P_(i) is implied by its predecessors: P₁, . . .P_(i−1)⇒P_(i). A discourse tree may be generated from text S. Adiscourse tree (DT) includes nodes and edges that represent rhetoricalrelationships between nodes representing fragments of the text. Eachnonterminal node represents a rhetorical relationship between two of thefragments. Each terminal node of the nodes of the discourse tree isassociated with one of the fragments. The fragments may be referred toas “elementary discourse units” (EDUs). A path within the discourse treemay be identified where these implications are realized via rhetoricalrelations. A mapping may be defined between EDUs of a DT and entitiesP_(i) occurring in these EDUs which can be used to form the explanationchain for the text S. In terms on underlying text, P_(i) are entitieswhich can be represented as logical atoms or terms. The explanationchain manager 124 may be configured to store the explanation chains itgenerates within database 114.

The machine-learning model 122 may be previously trained by any suitablesystem to identify output data from input data. The machine-learningmodel 122 may include one or more predictive models, classificationmodels, neural networks, and so on. In some embodiments,machine-learning model 122 may be trained utilizing any suitablesupervised learning algorithm in which a function (e.g., a model) istrained to identify output (e.g., a mortgage application decision) fromprovided input (e.g., user data such as the user's banking accountinformation, credit report, a loan application, etc.) based at least inpart on a training data set including input/output pairs (e.g., otheruser data paired with corresponding output decisions). Although themachine-learning model 122 is used in examples herein to determinemortgage loan application decisions, the machine-learning model 122 canbe utilized in any suitable context to provide any suitable decisionfrom input data. In some embodiments, the autonomous agent application108 may be configured to train the machine-learning model 122 fromtraining data 116 (e.g., a number of example user data (input)/decision(output) pairs), or the autonomous agent application 108 may obtain the(already trained) machine-learning model 122 from memory or anothersystem. In some embodiments, the output (e.g., a decision) provided bythe machine-learning model 122 may include a decision log which includesthe specific factors (e.g., specific user data) which influenced thedecision. In some embodiments, the output may be stored in database 118and/or the input utilized by the machine-learning model 122 and thecorresponding output provided by the machine-learning model 122 may bestored as additional training data with training data 116.

In an example, question answering engine 112 receives one or more userqueries from user device 106. Question answering engine 112 may beconfigured to analyze the user query to determine what type of userrequest the user query poses. At a high level, there can typically bethree types of requests: (1) A transactional request to perform someaction, (2) a request for general information, (referred to as “ageneral question”), and (3) a request for personalized information(e.g., a question that requests an answer based on the user's personalinformation, herein referred to as a “specific question”). Atransactional request corresponds to a response in which a unit of workis created and/or performed. To provide an answer to a general question,the question answering engine 112 may be configured to construct ananswer from its extensive knowledge base(s) (e.g., from the documentsstored in database 114). If the user has posed a specific question, thequestion answering engine 112 may be configured to construct an answerbased on the knowledge base obtained from the corpus of documents (e.g.,the explanation chains stored in database 114) as applied to specificinformation associated with the user (e.g., the decision log stored indatabase 118). The constructed answer may be provided at user device 106via user interface 130.

FIG. 2 depicts an example discourse tree 200 of an example explanation,in accordance with at least one embodiment.

Certain aspects of the disclosure include generating (e.g., by theexplanation chain manager 124 of FIG. 1 ) “complete discourse trees”(complete DT). Complete DTs may be utilized to determine anacceptability of an explanation to be provided by an autonomous agent. A“complete DT,” as used herein, is intended to refer to a sum of atraditional discourse tree (DT) for a portion (e.g., a paragraph) oftext and an imaginary DT for text corresponding to various entities thatare used but not explicitly defined in the actual text. Thus, theimaginary DT content cannot be produced from text as parsing results. Insome embodiments, the imaginary DT is inserted in the discourse tree asa sub-tree to form a complete DT.

Arcs of the discourse tree 200 correspond to rhetorical relations (RR),connecting text blocks called Elementary Discourse Units (EDU). In someembodiments, the discourse tree 200 is generated based at least in parton Rhetorical Structure Theory (RST, Mann and Thompson, 1988) anddescribes the discourse structure of the example explanation below. Eachnode in the discourse tree (e.g., nodes 202-214) corresponds to arhetorical relationship between two portions of text corresponding eachchild node. The leaves (e.g., leaves 216-230) of discourse tree 200 eachcorrespond to a particular portion of the text.

The example paragraph below provides an informal explanation in thebanking domain. In the banking domain nonsufficient fund fee (NSF) is amajor problem that banks have difficulties communicating with customers.The explanation includes the following:

-   -   It's not always easy to understand overdraft fees. When a        transaction drops your checking account balance below zero, what        happens next is up to your bank. A bank or credit union might        pay for the transaction or decline it and, either way, could        charge you a fee.        The concept of transaction is not addressed in this text        explaining overdraft fees. An ontology could specify that        transaction={wiring, purchasing, sending money} but such an        ontology is difficult to complete. Instead, one can complement        the notion of transaction via additional text that will        elaborate on transaction, providing more details on it.

When people explain concepts or ideas, they do not have to enumerate allpremises: some of them implicitly occurring in the explanation chain andare assumed by the person providing explanation to be known or believedby an addressee. However, a DT for a text containing an explanation onlyincudes EDUs from actual text and assumed, implicit parts with itsentities and phrases (which are supposed to enter explanation sequence)are absent.

In the example provided in FIG. 2 , an Elaboration relation for nucleustransaction (e.g., depicted by sub-tree 232) is not originally in thediscourse tree 200 but is likely assumed by a recipient of thisexplanation text. Such rhetorical relations may be referred to as“imaginary” as they are not produced from text but are instead inducedby the context of explanation. Such multiple imaginary RRs can begenerated to form additional nodes of the discourse tree 200. Thediscourse tree 200 of FIG. 2 may be considered complete as it combinesthe actual DT (e.g., discourse tree 200) and its imaginary part (e.g.,sub-tree 232). Complete discourse trees can also have communicativeactions attached to their edges in the form of VerbNet verb signatures(not depicted).

FIG. 3 depicts an example semantic parse of the example explanationcorresponding to FIG. 2 , in accordance with at least one embodiment.

A frame semantic parse for the same text is shown in FIG. 3 . It isdifficult, using a semantic parse to tag entities and determine contextproperly. Bank is tagged as Placing (not disambiguated properly) and‘credit union might’ is determined as a hypothetical event since unionis represented literally, as an organization, separately from credit.Overall, the main expression being explained, ‘transaction drops yourchecking account balance below zero’, is not represented as a cause of aproblem by semantic analysis, since a higher level considerationsinvolving a banking—related ontology would be required. FIG. 3illustrates that attempting to classify explanations using a semanticparse would be ineffective. In contrast, the discourse tree 200 appearsto be more suitable to classifying explanations.

FIG. 4A depicts another visualization of an example discourse tree(e.g., discourse tree 400) from which an explanation chain may begenerated, in accordance with at least one embodiment.

Valid explanations in text follow certain rhetoric patterns. In additionto default relations of Elaborations, valid explanation relies on Cause,Condition, and domain-specific Comparison. An example explanation forwhy thunder sound comes after lightning is provided below:

-   -   ‘We see the lightning before we hear the thunder. This is        because light travels faster than sound. The light from the        lightning comes to our eyes much quicker than the sound from the        lightning. So we hear it later than we see it.’        Discourse tree 400 may be generated for the explanation above.        Indentation for each line shows the tree hierarchy.

Logically, an explanation within text S may include a chain of premisesP₁, . . . , P_(m) which imply S. The chain of P₁, . . . , P_(m) may bereferred to as “an explanation chain.” S may be referred to as a subjectof the explanation. For this explanation chain P₁, . . . , P_(m) eachelement P_(i) is implied by its predecessors: P₁, . . . P_(i−1)⇒P_(i).In terms of a discourse tree, there should be a path in it where theseimplications are realized via rhetorical relations. A mapping may bedefined between Elementary Discourse Units (EDUs) of a DT and entitiesP_(i) occurring in these EDUs which can be used to form the explanationchain for the text. In terms on underlying text, P_(i) are entitieswhich can be represented as logical atoms or terms.

These implication-focused rhetorical relations (RR) may include:

-   -   1) elaboration: where P_(i) can be an elaboration of P_(i−1);    -   2) attribution: where P_(i) can be attributed to P_(i−1);    -   3) cause: this is a most straightforward case, where P_(i)        ⇒P_(j) if RR (EDU_(i), EDU_(j)) where P_(i∈)EDU_(i) and        P_(j∈)EDU_(j). This condition can be referred to as        “explainability” via Discourse Tree. The actual sequence P₁, . .        . , P_(m) for S is not known, but for each S we have a set of        good explanations P_(g1), . . . , P_(gm) and a set of bad        explanations P_(b1), . . . , P_(b2). Good explanation chains        obey the explainability via DT condition and bad explanation        chains do not. Bad explanation chains might obey the        explainability via DT condition for some P_(bi). If a DT for a        text is such that explainability via DT condition does not hold        for any P_(bi) then this DT does not include any explanation at        all.

Each fragment of text (e.g., fragments 402-412) may be characterized asdifferent premises and an explanation chain may be generated from thepremises of the body of text according to predefined rules. In someembodiments, the explanation chain may be analyzed to determine missingpremises (e.g., identifying when an entity such as a noun (“lightning”)lacks a logical connection to the entity such as another noun (“light”),identifying that the entity “thunder” lacks a logical connection to theentity “sound,” etc.).

One missing premise may correspond to a lack of logical connectionbetween “quicker” and “later.” Said another way, an implication ismissing between a verb-group-for-moving {moves, travels, comes}faster→verb-group-for-moving-result {earlier}. This clause can be easilyobtained by web mining, searching for expression ‘if nounverb-group-for-moving faster than noun verb-group-for-moving-resultearlier.”

In some embodiments, each missing premise may be identified bydetermining all entities Y in the explanation chain which do not occurin expression ‘Z because of Y, Y because of X’. If any of these premises(which correspond to fragments of text) are missing, they can beacquired via an imaginary DT.

For example, consider an explanation of text S is a chain of premisesP_(i), . . . , P_(m) which imply S. Each premise P_(i) contains just asingle entity corresponding to a text fragment. The set of premises canbe represented as a sequence of text fragments. ‘P_(m) because ofP_(m)=1, . . . P_(i+1) because of P_(i), P_(i) because of P_(i−1) . . ., P_(i−1) because of P_(i−2)’. For each missing premise, an imaginary DTmay be generated.

In some embodiments, the autonomous agent application 108 of FIG. 1 maybe configured to identify text corresponding to each missing premisewith which the discourse tree may be augmented. One example method isprovided in FIG. 5 .

FIG. 4B depicts an example complete discourse tree corresponding to thediscourse tree of FIG. 4A, in accordance with at least one embodiment.

Portions 414 and 416 are intended to relate to an imaginary discoursetree that has been generated for the missing premises of the discoursetree of FIG. 4A. In some embodiments, the imaginary discourse treeportions may be obtained, as disclosed herein, by utilizing a query(e.g., against an online search database, against a corpus of documents,etc.) to generate search results from which these imaginary discoursetree portions may be mined/obtained. The discourse tree of FIG. 4A andthe imaginary discourse tree portions corresponding to the missingpremises of FIG. 4A may be combined to form the complete discourse treedepicted in FIG. 4B.

FIG. 5 illustrates an exemplary method for generating an explanationchain, in accordance with at least one embodiment.

Method 500 can be performed by a computing device (e.g., the computingdevice 102 of FIG. 1 ). In some embodiments, the method 500 may beperformed by the explanation chain manager 124 of FIG. 1 . In someembodiments, the explanation chain manager 124 may be implemented by theautonomous agent 108 of FIG. 1 . In some embodiments, computing device102 need not execute explanation chain manager 124. Rather, theexplanation chain manager 124 could run on a separate device configuredto access the augmented training data generated by the computing deviceusing the method 500.

Method 500 may begin at block 501, where training data may be accessed.The training data may comprise a question and a body of textcorresponding to an explanation associated with the question. In someembodiments, the training data can comprise any suitable number ofquestions associated with corresponding explanations that areindividually included in a body of text.

At 502, a discourse tree may be constructed from the body of text. Insome embodiments, the discourse tree may represent rhetoricalrelationships between elementary discourse units of the body of text. Anelementary discourse unit may correspond to a leaf node (e.g., any oneof leaves 216-230 of FIG. 2 ) of a discourse tree (e.g., discourse tree200 of FIG. 2 ). In some embodiments, each elementary discourse unit maycomprise one or more entities (e.g., nouns, verbs, phrases, etc.).

At 503, it may be identified that a first entity of a first elementarydiscourse unit (e.g., a noun, verb, phrase, etc.) and a second entity ofa second elementary discourse unit (e.g., a noun, verb, phrase, etc.)have no logical connection. By way of example, as described above inconnection with FIG. 4 , an explanation chain may be generated from theelementary discourse units of the body of text. In some embodiments, theexplanation chain may include premises corresponding to the EDUs andindications of logical connections between the EDUs. Using theexplanation chain, the computing device can determine that the firstentity of a first elementary discourse unit and a second entity of asecond elementary discourse unit have no logical connection based ondetermining that a premise is missing that links the first entity to thesecond entity.

At 504, a query may be generated based on the first entity and thesecond entity. By way of example, one example query may be ‘if nounverb-group-for-moving faster than noun verb-group-for-moving-resultearlier.” A query, as used herein, may be a web query, a search queryused against a corpus of documents, and the like.

At 505, a candidate search result corresponding to the web query may beobtained. In some embodiments, multiple candidate search results may beidentified in response to executing the web query (e.g., by a web searchengine). In some embodiments, a set of discourse trees may be generatedcorresponding to each of the set of candidate search results. A subsetof the set of discourse trees may be identified that indicate a logicalrelationship that links the first fragment and the second fragmentwithin the explanation chain. Each of the subset of the set of discoursetrees may be scored based on a degree of relevancy between eachcandidate search result and the question. In some embodiments, thecandidate search result may be obtained when the discourse treecorresponding to the candidate search results is selected based on itsscore.

At 506, an additional discourse tree (e.g., sometimes referred to aboveas an imaginary discourse tree) may be generated from the candidatesearch result.

At 507, augmented training data may be generated based on modifying thetraining data to comprise an association between the question and acomplete discourse tree comprising the discourse tree and the additionaldiscourse tree. In some embodiments, the complete discourse tree may begenerated by combining the discourse tree and the additional discoursetree (e.g., an imaginary discourse tree that is obtained, not throughparsing the original text, but by mining search data). In someembodiments, the question may be additionally, or alternatively,associated with an explanation chain generated using the completediscourse tree.

At 508, a machine-learning model may be trained to identify explanationsfrom input questions. In some embodiments, the machine-learning modelmay be trained utilizing a supervised learning algorithm and theaugmented training data. For example, the augmented training data mayinclude various examples of a question, an explanation chain of a bodyof text from which an answer was provided, and the answer provided inresponse to the question.

As a non-limiting example, imaginary DTs may be built on demand toaugment the DTs built from the actual text. By way of example, a givenchain P_(i), . . . , P_(i)′, . . . , P_(m) let P_(i)′ be the entitywhich is not explicitly mentioned in a text but instead is assumed to beknown to the addressee. To make the explainability via DT conditionapplicable, the actual DT_(actual) can be augmented with imaginaryDT_(imaginary) such that P_(i)′∈EDU of this DT_(imaginary). We denoteDT_(actual) DT_(imaginary) as DT_(complete). If textual explanations areprovided in the positive set of good explanations for the same S, T₁ andT₂.

T₁: P₁, . . . , P_(m)⇒S

T₂: P₁, P_(i)′, . . . , P_(m)⇒S

then it can be assumed that P_(i)′ should occur in a completeexplanation for S and since it does not occur in T₁ then DT(T₁) shouldbe augmented with DT_(imaginary) such that P_(i)′∈EDU of thisDT_(imaginary).

It should be appreciated that, in some embodiments, the techniquesdiscussed above may be utilized to classify explanations as being either“complete” or “incomplete.” For example, in some embodiments, a body oftext corresponding to an explanation may be accessed. A discourse treemay be generated from the body of text (e.g., using rhetorical structuretheory). An explanation chain may be generated from the discourse tree'selementary discourse units (e.g., leaves of the discourse tree). In someembodiments, the explanation may be classified as “complete” when thereare no missing logical connections between the premises of theexplanation corresponding to the EDUs of the discourse tree.Alternatively, the explanation may be classified as “incomplete” whenthere are missing logical connections between the premises of theexplanation corresponding to the EDUs of the discourse tree.

Although FIG. 5 includes steps for generating a complete discourse tree,in some embodiments, an explanation chain may be generated from thediscourse tree (e.g., when the discourse tree is already complete)without generating an additional discourse tree (e.g., performing504-506).

FIG. 6 depicts a schematic diagram of computing components of a questionanswering engine 602 (an example of the question answering engine 112 ofFIG. 1 ), in accordance with at least one embodiment.

The question answering engine 602 may include a number of modules. Thesemodules may be software modules, hardware modules, or a combinationthereof. If the modules are software modules, the modules can beembodied on a computer readable medium and processed by a processor inany of the computer systems described herein. It should be noted thatany module or data store described herein, may be, in some embodiments,a service responsible for managing data of the type required to makecorresponding calculations. The modules, or some portion of the modules,may be operate as part of the autonomous agent application 108 of FIG. 1.

In the embodiment shown in the FIG. 6 , database 604 is shown, althoughdata can be maintained, derived, or otherwise accessed from various datastores, either remote or local to the question answering engine 602, toachieve the functions described herein. In at least one embodiment, thedata store described herein may be physically located on the computingdevice 102 of FIG. 1 , or alternatively, the data store may be localand/or otherwise accessible to the computing device 102. The questionanswering engine 602, as shown in FIG. 6 , includes various modules suchas a query type recognizer 606 and a dialogue manager 614. Somefunctions of the modules are described below. However, for the benefitof the reader, a brief, non-limiting description of each of the modulesis provided in the following paragraphs.

The query type recognizer 606 may be configured to classify a questionas being a general question (e.g., a question for which an answer doesnot depend on user data) or a specific question (e.g., a question forwhich an answer depends on user data). In some embodiments, a questioncan be classified into one of the following types:

-   -   1) General question: requests a general fact. (e.g., ‘Is my        interest rate up due to slow down in the economy?’, ‘Am I        charged a fee because the bank has to borrow money to cover my        negative balance?’, etc.);    -   2) Specific question:        -   a. where user data affects the answer: ‘Am I charged NSF            because my paycheck was delayed?’, ‘Did my interest rate go            up due to a high number of my credit card accounts?’;        -   b. Other kinds of specific questions such as ‘What is my            account balance?’;        -   c. Specific questions such as ‘Why’ questions associated            with user data: ‘Why was I charged NSF when my paycheck was            greater than what I paid for?’;    -   3) Request to perform a transaction (also referred to as a        transaction request).

In some embodiments, the user may provide a series of requests that caninclude a mixture of general questions, specific questions, and/ortransactional requests. By way of example, before a user wants torequest a particular personal financial information or perform an action(such as open a new bank account), she might ask a general question(e.g., what are the rules and conditions for this type of account?).Once the user is satisfied, she may make a decision and order atransaction (e.g., open a new bank account). Once this transaction iscompleted, the user might ask a specific question (such as ‘what is myaccount balance?’). Hence general questions, specific questions, andtransactional requests are intermittent and need to be recognizedreliably.

A request for general information, personal/specific information, or atransaction request can be formulated explicitly or implicitly. ‘Couldyou do this’ may mean both a question about the access control as wellas an implicit request to do this. Even a simple question ‘When is mypayment due? Pay now’ may be a factoid question or a transactionalrequest to select an account and execute a database query. Another wayto express a request is via mentioning of a desired state instead ofexplicit action to achieve it. For example, utterance ‘I am short offunds in my checking account #2’ indicates not a question but a desiredstate that can be achieved by transferring funds. To handle thisambiguity in a domain-independent manner, these types of questions andrequests may be differentiated linguistically. An utterance (e.g., auser query) may be classified as a transactional request or a question(e.g., a general question or specific question) based on two levels: 1)keyword level, and 2) linguistic analysis of phrases level).

In some embodiments, database 604 of the query type recognizer 606 mayinclude classification data such as question intent prefixes, mentalverbs, imperative verbs, request suffixes, request prefixes, requestverbs, stop prefixes, or any suitable data with which a user query maybe classified as a general question, specific question, and/ortransactional request. Other vocabulary words or words learned over timeare possible. Such classification data can be arranged in one or morearrays, lists, databases, or other data structures. By way of exampledatabase 604 may include expressions used by an utterance author toindicate the he wants an agent (e.g., the autonomous agent application108) to do something, such as ‘Please conduct . . . for me’. Theseexpressions also refer to information requests such as ‘Give me MY . . .’ (for example, current account yield). For a question, this vocabularyincludes the ways people address questions, such as ‘please tell me . .. ’.

The rules engine 608 may be configured to employ a set of rules (e.g., aset of transactional request identification rules, general questionidentification rules, specific question identification rules, etc.),including the keyword-based, vocabulary-based and linguistic onesagainst a user query to determine a classification for the user query(also referred to as “an utterance”). The rules may be applied in aparticular, predefined order, oriented to first find indication of atransactional request. In some embodiments, if the user query is notclassified by the rules engine 608 as being a transactional request, therules engine 608 may then employ a set of general question and/orspecific question identification rules. If the user query is notidentified as a question (e.g., general or specific), the user query maybe classified as ‘unknown’. By default if no rule applies to the userquery, the user query may be treated as a general question.

Linguistic processing engine 610 may be configured to perform one ormore stages of linguistic analysis of the utterance. For example,linguistic processing engine 610 can detect a presence of a leading verband a reference to an object in the possessive (e.g., “my house”).Linguistic engine 130 can parse the utterance and attempt to match theparsed utterance to one or more templates stored in database 604. Therules engine 608 may utilize these matches in order to identify whetherthe utterance is a general question, a specific question, or atransactional request. Examples of templates include string templatesand linguistic templates. String templates detect the presence of astring in an utterance, whereas linguistic templates determine apresence of a match of a one or more specific types of words (e.g.,noun, verb, pronoun).

In some embodiments, the linguistic processing engine 610 is designed towork with two templates: imperative leading verb and a reference to “MY”object. Once parsing is done, the first word should be a regular verb inpresent tense, active voice, neither modal, mental or a form of ‘be’.These constraints assure this verb is in the imperative form ‘Drop thetemperature in the room’. The second case addresses utterance related toan object the author owns or is associated too, such as ‘my accountbalance’ and ‘my car’. These utterances are connected with an intent toperform an action with these objects or request for an information onthem (versus a question which expresses a request to share generalknowledge, not about this particular, my object).

Linguistic processing engine 610 may identify one or more vocabularywords or template matches in an utterance by using the classificationdata described herein. In some embodiments, each match outcome can beweighted by a set of rules. As such, a particular match alone is notnecessarily deterministic of a particular classification.

In some embodiments, database 604 may store question intent prefixes(e.g., a portion of the classification data discussed herein). Questionintent prefixes may be utilized by the linguistic processing engine 610and may include prefix words that may be utilized to identify a questionwithin an utterance. For example, an utterance including “I want toknow” includes question intent prefix “I want.” Examples of intentprefixes are shown below in Table 1. Question intent prefixes mayinclude misspellings and informal variants that may be expected withshorthand or Internet-based communications.

TABLE 1 Question Intent Prefixes can you tell can you explain pleasetell please inform why how to how much can how do i do i how can what'sthe cause whats the cause what is the cause what s the cause what's thedifference whats the difference what is the difference what s thedifference what's the reason whats the reason what is the reason what sthe reason how much money show how show me how i need to know i want toknow where can

In some embodiments, database 604 may store a set of mental verbs (e.g.,a portion of the classification data discussed herein). Mental verbs mayinclude verbs that relate to mental activities. Examples include “know,”“think,” “recognize,” and so on. Mental verbs can include variants ofeach verb in a different tense (e.g., know, knew, thought, etc.). Upondetecting a presence of one or more mental verbs, rules engine 608 maybe configured to classify an utterance as a question.

The database 604 may include imperative verbs (e.g., a portion of theclassification data discussed herein). The linguistic processing engine610 can be utilized to identify within the utterance, the imperativeverbs, verb phrases, and variants such as misspellings, verbs withincorrect punctuation, or different tenses of the same verb. Upondetecting a presence of one or more imperative verbs by the linguisticprocessing engine 610, the rules engine can classify the utterance as atransactional request. Table 2 illustrates some examples of imperativeverbs and variants.

TABLE 2 Example Imperative Verb Phrases and Variants do not please donot don't please dont “don t” “please don t” i want to “i don t want to”“i don't want to” “i want you do” i dont want you to”, i don t want u to“can you could you can u could u what is my where are my when are my ineed i want it is is it what is the time what day what year

In some embodiments, linguistic processing engine 610 may determine thatthe first word in an utterance is either a verb in the present tense, averb in active voice, and neither modal, mental, or a form of the word“to be.” Such constraints ensure, for example, that the verb is in theimperative form, e.g., “drop the temperature in the room.”

In another aspect, linguistic processing engine 610 can also detectwhether an utterance is related to an object the author of the utteranceowns or is associated with. Examples include “my account balance” and“my car.” The rules engine 608 can utilize the existence of these typesof objects in the utterance to classify the utterance as being aspecific question rather than a general question that expresses arequest to share general knowledge not about the particular object(s).

In a further aspect, linguistic processing engine 610 can determineadditional verb variants. Verb variants include identifying additionalverbs such as with prefixes “re,” “dis,” or “un.” Examples include“re-load,” “re-heat,” “disassemble,” and “unmake.” In a further aspect,linguistic processing engine 610 maintains a list of imperative verbprefixes “can you” as in “can you turn on the light.” The rules engine608 can utilize the detection of can such prefixes to classify theutterance as a transactional request.

To assist with determining whether an utterance is a transactionalrequest, linguistic processing engine 610 can access a set requestsuffixes to determine whether any of these request suffixes occur withinthe utterance. Request suffixes include adjectives that describe astate, such as a state of an object. Table 3 depicts examples of requestsuffixes.

TABLE 3 Request Suffixes off on please hot cold

To assist with determining whether an utterance is a transactionalrequest, linguistic processing engine 610 can access a set of requestprefixes to determine whether any of these request prefixes occur withinthe utterance. Request prefixes include leading adjectives. Table 4depicts examples of transaction suffixes.

TABLE 4 Request Prefixes too more

Request verbs may include a set of verbs that indicate a transactionrequest or that do not indicate a transactional request. Examples ofrequest verbs may also include “get,” “set,” bring,” and “withdraw.” Iflinguistic processing engine 610 detects one or more of the verbs thatindicate a transaction request within the utterance, the rules engine608 may be configured to classify the utterance as a transactionalrequest. In some embodiments, the linguistic processing engine 610 candetect one or more verbs in the utterance that do not indicate atransactional request. In some embodiments, the rules engine 608 canignore the presence of such words in the utterance and/or the rulesengine 608 can utilize the presence of such words to determine that theutterance is not a transactional request. Table 5 depicts examples ofverbs that indicate transactional requests and an absence of atransactional request.

TABLE 5 Verbs indicating a Verbs not indicating a transactional requesttransactional request answer get consume burn approve take continue betavoid begin adjust dig base build adopt dream block break check drinksend give complain feel receive carry close forgive bill classifycomplete forget connect clear confine hear consider consume credit hurtcontinue contribute convert mean

Database 604 may store stop prefixes (e.g., a portion of theclassification data discussed herein). Stop prefixes include words orprefixes that a user might add to the beginning of an utterance such as“please.” In an aspect, stop prefixes can be removed by linguisticprocessing engine 610 so as to not interfere with other processing.Table 6 illustrates examples of stop prefixes.

TABLE 6 Stop Prefixes please I we kindly pls

Database 604 may store any suitable number of templates (e.g., a portionof the classification data discussed herein). In some embodiments, thelinguistic processing engine 610 applies one or more of these templatesto an utterance. Templates can include syntax-based templates orlinguistic templates. An example syntax-based template is whether anutterance contains “how to” or “if.”

In some embodiments, the rules engine 608 itself does not classify theutterance. Rather, in some embodiments, decision engine 612 may receiveoutput(s) of the rules engine 608 and/or the language processing engine610 and determines a classification for the utterance based on theoutput(s). If any of the components failed while applying a rule, theresultant decision may be determined to be “unknown”. In someembodiments, the rules engine 608 and/or the decision engine 612 may beconfigured to cause a set of operations to be performed in response toidentifying the utterance as containing a transaction request. The setof operations performed may depend on the particular transactionrequest. In some embodiments, the rules engine 608 and/or the decisionengine 612 may output (e.g., to a separate system, not depicted) dataindicating the utterance is a transaction request, and the output may beutilized (e.g., by the separate system) to determine one or moreoperations to be performed.

In some embodiments, the question answering engine 602 may includedialogue manager 614. The dialogue manager may be configured todetermine an answer for a question (e.g., a general question, a specificquestion, as determined by the query type recognizer 606). In someembodiments, the dialogue manager 614 may utilize a machine-learningmodel 616. Machine-learning model 616 may be previously trainedutilizing any suitable machine-learning techniques (e.g., supervisedlearning, unsupervised learning, reinforced learning, neural networks,deep learning, etc.) to identify an answer based at least in part on aquestion determined from the user query (e.g., the utterance). Themachine-learning model 616 may be previously trained to identify answersbased at least in part on training data 618 (e.g., example pairs ofquestions and corresponding answers). In some embodiments, themachine-learning model 616 may be trained using augmented training data(e.g., training data 618) as described in FIG. 5 . The training may beperformed by the explanation chain manager 124 of FIG. 1 , or a separatesystem that utilizes training data comprising examples of a question, anexplanation chain generated from a body of text from which an answer wasprovided, and the answer provided in response to the question.

In some embodiments, the dialogue manager 614 may be configured toutilize question answer templates. Some example question answertemplates are provided below.

-   -   General Question        -   General (factoid) Answer    -   FoC1 Question 1        -   confirmation and Factoid+FoC Answer    -   FoC2 Question        -   negation of FoC2+Factoid    -   Why.Decision_Log.FoC1 Question        -   Why.Decision_Log.FoC1 Answer+Why.Decision_Log.FoC2            Answer+temporal_sequence(Why.Decision_Log.FoC1)            These question/answer templates may be utilized by the            dialogue manager 614 to generate an answer to a question.            Some example questions and corresponding answers generated            from the corresponding question answer templates above are            provided below:    -   When am I getting an NSF fee?        -   You are charged a NSF fee when your balance goes below zero    -   Did my balance go below zero?        -   Yes. Your balance was −$24.43 on Monday, Jan. 7, 2019    -   Was my pay check used against my payments        -   No. Your balance was −$24.43. Only available funds in a            checking account can be used for your debit transaction    -   Why wasn't my paycheck used?        -   Your paycheck was debited in ATM on Sun Jan. 6, 2019 and            processed by the end of day Mon Jan. 7, 2019

FIG. 7 depicts a flowchart illustrating an example of a process 700 fordiscriminating between questions and a request, in accordance with atleast one embodiment.

In some embodiments, the process 700 may be performed by the query typerecognizer 606 of FIG. 6 .

At block 701, process 700 involves accessing an utterance including aword. Examples of utterances include short phrases such as “stop,”longer phrases such as “turn off the heat,” or “how do I check myportfolio?,” or “why did my loan application get rejected?” Query typerecognizer 606 can access an utterance from a process or application(e.g., autonomous agent application 108 of FIG. 1 ) executing oncomputing device 102 or from another computing device such as a userdevice (e.g., the user device 106 of FIG. 1 ).

In some embodiments, query type recognizer 606 (e.g., linguisticprocessing engine 610, a component of the query type recognizer 606)preprocesses an utterance to remove stop prefixes. Removing stopprefixes can prevent errors including ignoring words that are in asecond or subsequent position in the utterance but indicate a particularclassification.

At block 702, process 700 involves generating a parse tree for theutterance. By way of example, linguistic processing engine 610 maygenerate a parse tree. The parse tree may include any suitable number ofnodes. Each node may identified as a particular type of the types below,provided in Table 7 (although other types are possible).

TABLE 7 Notation Description S Sentence NP Noun Phrase VP Verb Phrase VVerb D or DOBJ determiner N Noun RP Phrasal verb particleStandard parsers can be used such as the Stanford NLP parser to generatethe parse tree at block 702.

At block 703, process 700 involves evaluating (e.g., by the rules engine608 of FIG. 6 ) one or more rules based on keyword or linguisticanalysis. Query type recognizer 606 can use the parse tree or theutterance (i.e., text) as input to any of the rules.

FIG. 8 depicts a flowchart illustrating examples of rules used fordiscriminating between questions (e.g., a general question, a specificquestion) and a transaction request, in accordance with at least oneembodiment.

Query type recognizer 606 (or rules engine 608, a component of Querytype recognizer 606) can execute one or more of blocks 801-805individually, in combination, and in any order. Different priorityorders are possible. For example, if query type recognizer 606determines that block 801 has successfully executed, then query typerecognizer 606 can output a classification of “transactional request,”and return to block 804. In another example, if query type recognizer606 executes block 502, but does not identify any transactional requestkeywords, then query type recognizer 606 can continue to one of blocks802-805.

At block 801, process 800 involves identifying one or more predefinedtransactional request keywords. Predefined transactional requestkeywords can include request suffixes, request prefixes, and requestverbs (see tables 3-5 above). A presence of one or more of thesekeywords indicates a transactional request. Table 8 depicts sentencesidentified as transactional requests and illustrates the analysisperformed in each case.

TABLE 8 Sentence Analysis Turn the light on request suffix “on” Putwiper rate on high request suffix “high” set the security system requestsuffix “off” to off too loud, quieter please request prefix “too”

More specifically, each type of the transactional request keywords canhave an associated position in which the keyword is expected. Forexample, query type recognizer 606 searches for a request prefix in thefirst word position of the utterance, request suffixes in the last wordposition in the utterance, and request verbs at any position in theutterance.

At block 802, process 800 involves determining that a first terminalnode of the parse tree includes an imperative verb. The query typerecognizer 606 (e.g., the linguistic processing engine 610) generates aparse tree representing the utterance and identifies an imperative verbfrom a set of predefined imperative verbs (see table 2 above). If aleading imperative verb, or a verb in the first word position of theutterance, is identified, then query type recognizer 606 can output aclassification of “transactional request,” and processes 500 and 400 canterminate. Table 9 depicts examples of utterances identified as requestsbased on a presence of imperative verb.

TABLE 9 Sentence Imperative Verb Open a reoccurring deposit open Cancela reoccurring deposit cancel Help me to log in help Transfer funds fromtransfer checking to saving Move funds from saving to move mortgageClose the garage door close do western union do

In an embodiment, query type recognizer 606 can weigh different factors.For example, in the case that query type recognizer 606 (e.g.,linguistic processing engine 610) detects a presence of a leading verb,indicating a request, the presence of “how” in the utterance, can negatea presence of a leading verb and indicate a question. In that case,query type recognizer 606 (e.g., rules engine 608, a component of thequery type recognizer 606) may classify the utterance as a question.

Additionally, query type recognizer 606 can detect the presence of afirst-person pronoun such as “me” or “my.” More specifically, in a casein which a leading verb is a mental verb, typically indicating aquestion, the presence of “me” or “my” can nevertheless indicate arequest. Therefore, in the case of a mental verb in conjunction with“me” or “my,” query type recognizer 606 classifies the utterance as atransactional request. Table 10 depicts examples of utterancesidentified with these rules.

TABLE 10 Sentence Analysis Give me checks [leading imperative verb +deposited in Bank me] Account but not credited leading verb identified“give,” and a presence of “me” identified. Classified as a transactionalrequest. Fund my investment Leading verb identified account fromchecking “fund” and “my” identified in an absence of “how.” Classifiedas a transactional request. Wire money from my Leading verb identifiedchecking to investment “fund” and “my” identified in an absence of“how.” Classified as a transactional request. Thinking about checkingLeading mental verb. accounts. Classified as general question. Thinkingabout my Leading mental verb checking account. “thinking” in combinationwith “my.” Classified as a specific question.

If no leading imperative verb match is found, then process 800 cancontinue to one or more of block 801, or 803-805 for further evaluation.

At block 803, process 800 involves applying, to the parse tree, one ormore linguistic templates and determining a linguistic template match.The linguistic template can include one or more word types (e.g., verb).More specifically, query type recognizer 606 (e.g., the linguisticprocessing engine 610) determines a match by determining that the one ormore word types are present in the parse tree. An example of a templateis a presence of a pronoun followed by a noun (represented by PR+NN). Amatch of this template can indicate a transactional request. Forexample, “give me my balance” or “get me my balance,” where the pronounis “my” and the noun is “balance.” Conversely, query type recognizer 606does not categorize the utterance “tell me how to check an accountbalance” as a transactional request due to the absence of the pronoun.

At block 804, process 800 involves identifying one or more predefinedquestion keywords. Examples of question keywords include question intentprefixes (see table 1) and mental verbs. A presence of one or more ofthese keywords indicates that the utterance is a question. If thekeywords further includes “my” (as identified at block 806), thequestion may be classified as a “specific question,” else the questionmay be classified as a general question. Table 10 depicts examples ofsentences in which one or more question keywords are identified byclassification application 102.

TABLE 10 Sentence Analysis I am anxious about [mental verb] classifiedas spending my money a specific question I am worried about my [mentalverb] classified as spending a specific question I am concerned abouthow [mental verb] classified as much I used a specific question I aminterested how much [mental verb] classified as money I lost on stock aspecific question How can my saving [How + my] classified as a accountbe funded specific question If I do not have my Internet [if and “howcan I”- Banking User ID and prefix] classified as a Password, how can Ilogin? general question

In some cases, query type recognizer 606 can default to a particularclassification if the rules for other classifications are not applied.For example, if no requests are identified in the utterance “domesticwire transfer,” then query type recognizer 606 identifies the utteranceas a general question. Table 11 identifies additional cases.

TABLE 11 Sentence Analysis Domestic wire transfer [no transactional rulefired therefore classified as a general question] orderreplacement/renewal [no transactional rule fired card not receivedtherefore classified as a general question]

At block 805, process 800 involves failing to identify the utterance asa question (general or specific) or a transactional request. If no rulesuccessfully identifies the utterance as a question or transactionalrequest, then autonomous agent application 108 of FIG. 1 can ask theuser for further clarification.

Returning to FIG. 7 , process 700 involves outputting the classificationat 704. The autonomous agent application 108 can receive theclassification and take action accordingly. Based on an identifiedclassification, autonomous agent application 108 can take some actionsuch as further interaction with user device 106.

FIG. 9 depicts a flowchart illustrating an example of a process 900 fortraining a classification model to determine informative text forindexing, in accordance with an aspect.

In process 900, a classification model can be trained to discriminatebetween questions (general or specific) and transactional requests.Training data for such a classification model can include multipletraining sets, such as a training set with text identified astransactional requests, another training set with text identified asgeneral questions, and yet another training set with text identified asspecific questions. In some embodiments, the training data can includetext and/or associated parse trees of the various instances of thetraining sets.

At block 901, process 900 involves accessing a set of training datacomprising a set of training pairs. Each training data pair comprisestext (or a parse tree of the text) and a predefined classification(e.g., general question, specific question, transactional request). Thetext (or parse tree) may be an example user query for which aclassification has previously been determined.

At block 902, process 900 involves providing one of the training datapairs to the classification model. Accordingly, the classification modelreceives a body of text and the expected classification.

At block 903, process 900 involves receiving a determined classificationfrom the classification model.

At block 904, process 900 involves calculating a loss function bycalculating a difference between the determined classification and theexpected classification. Different loss functions are possible such asmean-square error, likelihood loss, log (or cross entropy) loss, etc.

At block 905, process 900 involves adjusting internal parameters of theclassification model to minimize the loss function. In this manner, theclassification model learns to improve the accuracy of its predictionswith each iteration of training.

At block 906, process 900 involves using the trained classificationmodel. For example, the trained classification model can be used by thequery type recognizer 606 (e.g., in lieu of or in addition to utilizingthe functionality of linguistic processing engine 610 and/or rulesengine 608 and/or decision engine 612) to identify whether an utteranceindicates a general question, a specific question, or a transactionalrequest.

For example, to use the trained classification model for identifying anutterance indicates a general question, a specific question, or atransactional request, query type recognizer 606 can access an utteranceof text and generate a parse tree for the utterance. Query typerecognizer 606 determines a classification of the utterance by applyingthe classification model to the parse tree.

In turn, query type recognizer 606 can use one of several methods todetermine a classification. For example, classification model maydetermine a first similarity score indicating a first match between theutterance and training example(s) identified as a general question, asecond similarity score indicating a second match between the utteranceand training example(s) identified as a specific question, and a thirdsimilarity score indicating a third match between the utterance andtraining example(s) identified as a transactional request.

The classification model may output a classification based on the firstsimilarity score, the second similarity score, and the third similarityscore. For example, if the first similarity score is higher than thesecond and third similarity scores, then classification model may outputa classification of “general question.” If the second similarity scoreis higher than the first and third similarity scores, thenclassification model may output a classification of “specific question.”If the third similarity score is higher than the first and secondsimilarity scores, then classification model may output a classificationof “transactional request.” In some cases, for example, if an erroroccurs, then classification model can output a classification of“unknown.”

FIG. 10 depicts a flow diagram of an example flow 1000 for providingconversational explanations, in accordance with at least one embodiment.

The flow 1000 begins at step 1, documents 1014 may be obtained (e.g., bythe explanation chain manager 124 of FIG. 1 ). Documents 1014 may be anysuitable documents obtained from the Internet, an intranet, or anysuitable document database. The documents 1014 may provide a knowledgebase from which explanations may be determined.

At step 2, the explanation chain manager 124 may perform document todialogue conversion. In some embodiments, this may include generatingquestion/answer pairs from the documents 1014. The proposed solutionuses discourse trees in conjunction with classification, syntacticgeneralization, and web-mining to determine questions and answer pairs.One method for performing document to dialog conversion may include thefollowing steps:

-   -   1. Construct a discourse tree (or a communicative discourse        tree) from a paragraph of text.    -   2. Identify rhetorical relation nucleus and satellite elementary        discourse units within the discourse tree.    -   3. For each satellite elementary discourse unit:        -   a. Convert the candidate into question form            -   i. Build a parse tree            -   ii. Select parse tree nodes for nouns, verbs and                adjectives. Also add nodes linked by co-references                (e.g., pronouns).            -   iii. For every selected node, form a reduction of a                parse tree by removing this node.            -   iv. Build a question for this reduction by substitution                a Wh word for this node            -   v. Select a proper Wh word following the rules: noun→Who                or What, verb→‘what . . . do’, adjective ‘Which way’,                ‘How is’.        -   b. Identify whether the candidate is in a suitable question            form (by using a trained machine learning model)        -   c. Generalize the candidate by performing syntactic            generalization        -   d. Confirm the question as valid by obtaining an answer via            a search engine and verifying the answer against the            original satellite educational unit    -   4. If the question is valid, place the question and the        corresponding answer in a database.        More information related to performing a document to dialog        conversion (e.g., generating question/answer pairs from text) is        discussed in further detail in provisional patent application        62/894,162, entitled, “Converting a Document into        Chatbot-Accessible form,” filed on Aug. 30, 2019, the entire        contents of which are incorporated by reference for all        purposes.

At step 3, a number of explanation chains may be generated (e.g., by theexplanation chain manager 124 of FIG. 1 ). In some embodiments, theexplanation chains may be generated from the documents 1014 as part ofan offline process utilizing the techniques discussed above inconnection with any suitable combination of the FIGS. 2-5 .

At step 4, the documents 1014 may be stored in search index 1010 (e.g.,a data store configured to store such information). In some embodiments,the documents 1014 may be indexed with the explanation chains,questions, and answers generated from the documents 1014 at steps 2 and3.

At step 5, data 1002 may be obtained. Data 1002 may include any suitableinput data which may be utilized by the machine-learning model 1004 asinput to determine data feature(s) 1006 and/or final decision(s) 1008.The combination of data feature(s) 1006 and final decision(s) 1008 maybe referred to herein as a decision log. As a specific example, the flow1000 may correspond to a process for providing conversationalexplanations for why a user's loan application was denied. In thiscontext, the data 1002 may include a particular user's banking accountinformation and transactional history, credit score(s) and/or creditreport(s), one or more tax documents associated with the particularuser, a loan application associated with the user, and/or the like.

At step 6, the data 1002 may be provided to the machine-learning model1004 as input. In some embodiments, the machine-learning model 1004 maybe previously trained (e.g., utilizing any suitable machine-learningtechnique such as supervised learning, unsupervised learning, reinforcedlearning, deep learning, neural networks, etc.) using predefinedtraining data (not depicted). Such training data may include exampleinput/output pairs including input data and output data comprising adecision. The specifics of the training data may depend on the contextin which machine-learning model 1004 is utilized. As a non-limitingexample, machine-learning model 1004 may be a model that has beentrained to take as input user data (e.g., various instances of user dataincluding banking account information and transactional history, creditscore(s) and/or credit report(s), one or more tax documents, and loanapplication(s) associated with corresponding users, etc.) to determinewhether to approve or deny this particular user a loan (e.g., whether toapprove or deny the user's loan application). In this context, thetraining data for machine-learning model 1004 may include any suitablenumber of examples of user data, data features identified from the userdata, and final decisions corresponding to the user data example.

At step 7, the machine-learning model 1004 may identify a number of datafeatures (e.g., data feature(s) 1006) that were factored in whendetermining a final decision. By way of example, a data feature(s) 1006may include data indicating one or more portions of user data (and itscorresponding value(s)) that were considered when determining adecision. In the ongoing loan application example, one data feature mayinclude the fact the user's income was $100,000 per year (e.g., asdetermined from the user's tax documents and/or loan application).Another data feature may be the age of the user's account (e.g., asdetermined from the user's credit report and/or loan application). Yetanother data feature may be the balances of the user's credit card(s)(e.g., as determined from the user's credit report and/or loanapplication).

At step 8, the machine-learning model 1004 may generate a final decision(e.g., the loan application is approved or denied) for this particularuser's loan application based on the lessons and/or featured learnedfrom the training data.

At step 9, the data feature(s) 1006 and the final decision(s) 1008 maybe stored in search index 1010 (e.g., in data store 1012 within searchindex 1010). Steps 5-9 may be performed as an online classificationstage.

At step 10, a user query 1016 may be received (e.g., from the userdevice 106 of FIG. 1 ). The user query 1016 may include a generalquestion, a specific question, a transactional request, or the like.

At step 11, the user query 1016 may be classified (e.g., by the querytype recognizer 606 of FIG. 6 , a component of the question answeringengine 112 of FIG. 1 , operating as part of the autonomous agentapplication 108 of FIG. 1 ). The user query 1016 may be classified usingany suitable technique discussed above in connection with FIGS. 7-9 (orany suitable combination of said techniques).

At step 12, an answer (e.g., answer 1018) may be generated for the userquery 1016 when the user query 1016 includes a question (e.g., a generalquestion, a specific question). By way of example, if the user query1016 was classified as a “general question,” an answer may be identifiedfrom the documents 1014 and/or the corresponding explanation chains,questions, and answers generated from the documents 1014 at steps 2 and3. For example, the dialogue manager 614 may be configured to generatean answer based on a machine-learning model (e.g., machine-learningmodel 616) that has been previously trained with training data (e.g.,documents and corresponding explanation chains, questions, and answersgenerated from the documents) to identify an answer based on a inputquestion. In some embodiments, the dialogue manager 614 may generate ananswer that is modified based at least in part on the data feature(s)1006 and/or final decision(s) 1008 provided by the machine-learningmodel 1004 as stored in data store 1012. For example, the dialoguemanager 614 may be configured to correlate portions of the explanationchains to particular features of the data feature(s) 1006. Thus,particular terms of the explanation may be replaced by the user'sparticular feature data values based on these associations.

At step 13, the answer (e.g., answer 1018) generated by the dialoguemanager 614 may be provided (e.g., to the user device 106 of FIG. 1 ,via the user interface 130).

FIG. 11 depicts a flowchart illustrating an example of a method 1100 forproviding conversational explanations, in accordance with at least oneembodiment.

In some embodiments, the method 1100 may be performed by the autonomousagent application 108 of FIG. 1 .

The method 1100 may begin at 1101, where a user query (also referred toherein as an utterance) may be received. By way of example, the userquery may be received by the query type recognizer 606 of FIG. 6 (acomponent of the question answering engine 602 operating as part of theautonomous agent application 108 of FIG. 1 ) from the user device 106 ofFIG. 1 .

At block 1102, where a classification for the user query may bedetermined (e.g., by the rules engine 608 and/or the decision engine 612of FIG. 6 ). The classification may be determined based at least in parton a predefined rule set as described in connection with FIGS. 7 and/or8 . Alternatively, as described in FIG. 9 , a classification model canbe trained to discriminate between questions (general or specific) andtransactional requests using the process described in connection withFIG. 9 , and the user query (or a parse tree of the user query) may beprovided as input to the classification model and output from the modelmay be received that identifies the user query as a general question, aspecific question, a transactional request, or unknown. By default, auser query determined to be “unknown” may be, by default, treated as ageneral question (or another classification) based on predeterminedrules.

At block 1103, a set of decision features associated with a decisiongenerated by a machine-learning model (e.g., the machine-learning model1004 of FIG. 10 , machine-learning model 122 of FIG. 1 , etc.) may beidentified. In some embodiments, the decision may be identified based atleast in part on the user query and the classification. For example,when the user query is identified as a specific question (e.g., why wasmy loan application rejected?), the autonomous agent application 108 maybe configured to retrieve a decision log comprising the decisionfeatures utilize by a machine-learning model that generated a decision(e.g., the user's loan application was rejected) corresponding to theuser query.

At block 1104, an explanation chain may be identified (e.g., selected bythe dialogue manager 614 of FIG. 6 ) from a plurality of explanationchains based at least in part on the user query. In some embodiments,the explanation chain may describe a logical chain of explanationsassociated with a decision making process related to themachine-learning model. By way of example, when the user query is “whywas my loan application rejected?” the autonomous agent application 108may be configured to identify an explanation chain relating to reasonsfor which a loan application may be rejected.

At block 1105, a response may be provided (e.g., by the dialogue manager614) to the user query based at least in part on the explanation chainand the set of decision features. By way of example, an associationbetween a premise P of the explanation chain may be determined to beassociated with a particular decision feature. If the decision featuremeets (or in some cases, does not meet) the premise provided by theexplanation chain, the decision feature (e.g., including the user data)may be provided in the explanation generated. The explanation mayinclude multiple decision features. An example in which an explanationprovided as an answer includes multiple decision features is providedbelow (see italicized answer).

-   -   Question: Why was my mortgage application denied?”    -   Answer: Some reasons your application was denied is that your        consolidated risk markers score is low and the average age of        your accounts is low.    -   Question: What can I do about it?    -   Answer: You should pay down the balances of your credit cards.    -   Question: What can I do about the average age of my accounts?    -   Answer: There's not much you can do except wait for your        accounts to age.

FIG. 12 depicts a simplified diagram of a distributed system 1200 forimplementing one of the aspects. In the illustrated aspect, distributedsystem 1200 includes one or more client computing devices 1202, 1204,1206, and 1208, which are configured to execute and operate a clientapplication such as a web browser, proprietary client (e.g., OracleForms), or the like over one or more network(s) 1210. Server 1212 may becommunicatively coupled with remote client computing devices 1202, 1204,1206, and 1208 via network(s) 1210.

In various aspects, server 1212 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. The services or software applications caninclude non-virtual and virtual environments. Virtual environments caninclude those used for virtual events, tradeshows, simulators,classrooms, shopping exchanges, and enterprises, whether two- orthree-dimensional (3D) representations, page-based logical environments,or otherwise. In some aspects, these services may be offered asweb-based or cloud services or under a Software as a Service (SaaS)model to the users of client computing devices 1202, 1204, 1206, and/or1208. Users operating client computing devices 1202, 1204, 1206, and/or1208 may in turn utilize one or more client applications to interactwith server 1212 to utilize the services provided by these components.

In the configuration depicted in the figure, the software components1218, 1220 and 1222 of system 1200 are shown as being implemented onserver 1212. In other aspects, one or more of the components of system1200 and/or the services provided by these components may also beimplemented by one or more of the client computing devices 1202, 1204,1206, and/or 1208. Users operating the client computing devices may thenutilize one or more client applications to use the services provided bythese components. These components may be implemented in hardware,firmware, software, or combinations thereof. It should be appreciatedthat various different system configurations are possible, which may bedifferent from distributed system 1200. The aspect shown in the figureis thus one example of a distributed system for implementing an aspectsystem and is not intended to be limiting.

Client computing devices 1202, 1204, 1206, and/or 1208 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 1202, 1204,1206, and 1208 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s)1210.

Although exemplary distributed system 1200 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 1212.

Network(s) 1210 in distributed system 1200 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 1210 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 1210 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 802.6 suite ofprotocols, Bluetooth®, and/or any other wireless protocol); and/or anycombination of these and/or other networks.

Server 1212 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 1212 caninclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization. One or moreflexible pools of logical storage devices can be virtualized to maintainvirtual storage devices for the server. Virtual networks can becontrolled by server 1212 using software defined networking. In variousaspects, server 1212 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 1212 may correspond to a server for performingprocessing described above in accordance with an aspect of the presentdisclosure.

Server 1212 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 1212 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include, without limitation, thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 1212 may include one or moreapplications to analyze and consolidate data feeds and/or event updatesreceived from users of client computing devices 1202, 1204, 1206, and1208. As an example, data feeds and/or event updates may include, butare not limited to, Twitter® feeds, Facebook® updates or real-timeupdates received from one or more third party information sources andcontinuous data streams, which may include real-time events related tosensor data applications, financial tickers, network performancemeasuring tools (e.g., network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like. Server 1212 may also include one or moreapplications to display the data feeds and/or real-time events via oneor more display devices of client computing devices 1202, 1204, 1206,and 1208.

Distributed system 1200 may also include one or more databases 1214 and1216. Databases 1214 and 1216 may reside in a variety of locations. Byway of example, one or more of databases 1214 and 1216 may reside on anon-transitory storage medium local to (and/or resident in) server 1212.Alternatively, databases 1214 and 1216 may be remote from server 1212and in communication with server 1212 via a network-based or dedicatedconnection. In one set of aspects, databases 1214 and 1216 may reside ina storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 1212 may be stored locallyon server 1212 and/or remotely, as appropriate. In one set of aspects,databases 1214 and 1216 may include relational databases, such asdatabases provided by Oracle, that are adapted to store, update, andretrieve data in response to SQL-formatted commands.

FIG. 13 is a simplified block diagram of one or more components of asystem environment 1300 (e.g., a cloud infrastructure system) by whichservices provided by one or more components of an aspect system may beoffered as cloud services, in accordance with an aspect of the presentdisclosure. In the illustrated aspect, system environment 1300 includesone or more client computing devices 1304, 1306, and 1308 that may beused by users to interact with a cloud infrastructure system 1302 thatprovides cloud services. The client computing devices may be configuredto operate a client application such as a web browser, a proprietaryclient application (e.g., Oracle Forms), or some other application,which may be used by a user of the client computing device to interactwith cloud infrastructure system 1302 to use services provided by cloudinfrastructure system 1302.

It should be appreciated that cloud infrastructure system 1302 depictedin the figure may have other components than those depicted. Further,the aspect shown in the figure is only one example of a cloudinfrastructure system that may incorporate an aspect of the invention.In some other aspects, cloud infrastructure system 1302 may have more orfewer components than shown in the figure, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

Client computing devices 1304, 1306, and 1308 may be devices similar tothose described above for 1202, 1204, 1206, and 1208 of FIG. 12 .

Although exemplary system environment 1300 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 1302.

Network(s) 1310 may facilitate communications and exchange of databetween client computing devices 1304, 1306, and 1308 and cloudinfrastructure system 1302. Each network may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including those described above for network(s) 1310.

Cloud infrastructure system 1302 may comprise one or more computersand/or servers that may include those described above for server 1212 ofFIG. 12 .

In certain aspects, services provided by the cloud infrastructure systemmay include a host of services that are made available to users of thecloud infrastructure system on demand, such as online data storage andbackup solutions, Web-based e-mail services, hosted office suites anddocument collaboration services, database processing, managed technicalsupport services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain aspects, cloud infrastructure system 1302 may include a suiteof applications, middleware, and database service offerings that aredelivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

Large volumes of data, sometimes referred to as big data, can be hostedand/or manipulated by the infrastructure system on many levels and atdifferent scales. Such data can include data sets that are so large andcomplex that it can be difficult to process using typical databasemanagement tools or traditional data processing applications. Forexample, terabytes of data may be difficult to store, retrieve, andprocess using personal computers or their rack-based counterparts. Suchsizes of data can be difficult to work with using most currentrelational database management systems and desktop statistics andvisualization packages. They can require massively parallel processingsoftware running thousands of server computers, beyond the structure ofcommonly used software tools, to capture, curate, manage, and processthe data within a tolerable elapsed time.

Extremely large data sets can be stored and manipulated by analysts andresearchers to visualize large amounts of data, detect trends, and/orotherwise interact with the data. Tens, hundreds, or thousands ofprocessors linked in parallel can act upon such data in order to presentit or simulate external forces on the data or what it represents. Thesedata sets can involve structured data, such as that organized in adatabase or otherwise in accordance with a structured model, and/orunstructured data (e.g., emails, images, data blobs (binary largeobjects), web pages, complex event processing). By leveraging an abilityof an aspect to relatively quickly focus more (or fewer) computingresources upon an objective, the cloud infrastructure system may bebetter available to carry out tasks on large data sets based on demandfrom a business, government agency, research organization, privateindividual, group of like-minded individuals or organizations, or otherentity.

In various aspects, cloud infrastructure system 1302 may be adapted toautomatically provision, manage and track a customer's subscription toservices offered by cloud infrastructure system 1302. Cloudinfrastructure system 1302 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 1302 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 1302 isoperated solely for a single organization and may provide services forone or more entities within the organization. The cloud services mayalso be provided under a community cloud model in which cloudinfrastructure system 1302 and the services provided by cloudinfrastructure system 1302 are shared by several organizations in arelated community. The cloud services may also be provided under ahybrid cloud model, which is a combination of two or more differentmodels.

In some aspects, the services provided by cloud infrastructure system1302 may include one or more services provided under a Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 1302. Cloud infrastructure system 1302 then performs processingto provide the services in the customer's subscription order.

In some aspects, the services provided by cloud infrastructure system1302 may include, without limitation, application services, platformservices and infrastructure services. In some examples, applicationservices may be provided by the cloud infrastructure system via a SaaSplatform. The SaaS platform may be configured to provide cloud servicesthat fall under the SaaS category. For example, the SaaS platform mayprovide capabilities to build and deliver a suite of on-demandapplications on an integrated development and deployment platform. TheSaaS platform may manage and control the underlying software andinfrastructure for providing the SaaS services. By utilizing theservices provided by the SaaS platform, customers can utilizeapplications executing on the cloud infrastructure system. Customers canacquire the application services without the need for customers topurchase separate licenses and support. Various different SaaS servicesmay be provided. Examples include, without limitation, services thatprovide solutions for sales performance management, enterpriseintegration, and business flexibility for large organizations.

In some aspects, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include, without limitation, servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someaspects, platform services provided by the cloud infrastructure systemmay include database cloud services, middleware cloud services (e.g.,Oracle Fusion Middleware services), and Java cloud services. In oneaspect, database cloud services may support shared service deploymentmodels that enable organizations to pool database resources and offercustomers a Database as a Service in the form of a database cloud.Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain aspects, cloud infrastructure system 1302 may also includeinfrastructure resources 1330 for providing the resources used toprovide various services to customers of the cloud infrastructuresystem. In one aspect, infrastructure resources 1330 may includepre-integrated and optimized combinations of hardware, such as servers,storage, and networking resources to execute the services provided bythe PaaS platform and the SaaS platform.

In some aspects, resources in cloud infrastructure system 1302 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 1302 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain aspects, a number of internal shared services 1332 may beprovided that are shared by different components or modules of cloudinfrastructure system 1302 and by the services provided by cloudinfrastructure system 1302. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain aspects, cloud infrastructure system 1302 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one aspect, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 1302, and the like.

In one aspect, as depicted in the figure, cloud management functionalitymay be provided by one or more modules, such as an order managementmodule 1320, an order orchestration module 1322, an order provisioningmodule 1324, an order management and monitoring module 1326, and anidentity management module 1328. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 1334, a customer using a client device, such asclient computing devices 1304, 1306 or 1308, may interact with cloudinfrastructure system 1302 by requesting one or more services providedby cloud infrastructure system 1302 and placing an order for asubscription for one or more services offered by cloud infrastructuresystem 1302. In certain aspects, the customer may access a cloud UserInterface (UI), cloud UI 1312, cloud UI 1314 and/or cloud UI 1316 andplace a subscription order via these UIs. The order information receivedby cloud infrastructure system 1302 in response to the customer placingan order may include information identifying the customer and one ormore services offered by the cloud infrastructure system 1302 in whichthe customer intends to subscribe.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 1312, 1314 and/or 1316.

At operation 1336, the order is stored in order database 1318. Orderdatabase 1318 can be one of several databases operated by cloudinfrastructure system 1302 and operated in conjunction with other systemelements.

At operation 1338, the order information is forwarded to an ordermanagement module 1320. In some instances, order management module 1320may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 1340, information regarding the order is communicated to anorder orchestration module 1322. Order orchestration module 1322 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 1322 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 1324.

In certain aspects, order orchestration module 1322 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning. At operation 1342, upon receiving an order for a newsubscription, order orchestration module 1322 sends a request to orderprovisioning module 1324 to allocate resources and configure thoseresources needed to fulfill the subscription order. Order provisioningmodule 1324 enables the allocation of resources for the services orderedby the customer. Order provisioning module 1324 provides a level ofabstraction between the cloud services provided by system environment1300 and the physical implementation layer that is used to provision theresources for providing the requested services. Order orchestrationmodule 1322 may thus be isolated from implementation details, such aswhether or not services and resources are actually provisioned on thefly or pre-provisioned and only allocated/assigned upon request.

At operation 1344, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientcomputing devices 1304, 1306 and/or 1308 by order provisioning module1324 of cloud infrastructure system 1302.

At operation 1346, the customer's subscription order may be managed andtracked by an order management and monitoring module 1326. In someinstances, order management and monitoring module 1326 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain aspects, system environment 1300 may include an identitymanagement module 1328. Identity management module 1328 may beconfigured to provide identity services, such as access management andauthorization services in system environment 1300. In some aspects,identity management module 1328 may control information about customerswho wish to utilize the services provided by cloud infrastructure system1302. Such information can include information that authenticates theidentities of such customers and information that describes whichactions those customers are authorized to perform relative to varioussystem resources (e.g., files, directories, applications, communicationports, memory segments, etc.). Identity management module 1328 may alsoinclude the management of descriptive information about each customerand about how and by whom that descriptive information can be accessedand modified.

FIG. 14 illustrates an exemplary computer system 1400, in which variousaspects may be implemented. The system 1400 may be used to implement anyof the computer systems described above. As shown in the figure,computer system 1400 includes a processing unit 1404 that communicateswith a number of peripheral subsystems via a bus subsystem 1402. Theseperipheral subsystems may include a processing acceleration unit 1406,an I/O subsystem 1408, a storage subsystem 1418 and a communicationssubsystem 1424. Storage subsystem 1418 includes tangiblecomputer-readable storage media 1422 and a system memory 1410.

Bus subsystem 1402 provides a mechanism for letting the variouscomponents and subsystems of computer system 1400 communicate with eachother as intended. Although bus subsystem 1402 is shown schematically asa single bus, alternative aspects of the bus subsystem may utilizemultiple buses. Bus subsystem 1402 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P886.1 standard.

Processing unit 1404, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 1400. One or more processorsmay be included in processing unit 1404. These processors may includesingle core or multicore processors. In certain aspects, processing unit1404 may be implemented as one or more independent processing units 1432and/or 1434 with single or multicore processors included in eachprocessing unit. In other aspects, processing unit 1404 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various aspects, processing unit 1404 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processingunit 1404 and/or in storage subsystem 1418. Through suitableprogramming, processing unit 1404 can provide various functionalitiesdescribed above. Computer system 1400 may additionally include aprocessing acceleration unit 1406, which can include a digital signalprocessor (DSP), a special-purpose processor, and/or the like.

I/O subsystem 1408 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system1400 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 1400 may comprise a storage subsystem 1418 thatcomprises software elements, shown as being currently located within asystem memory 1410. System memory 1410 may store program instructionsthat are loadable and executable on processing unit 1404, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 1400, systemmemory 1410 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 1404. In some implementations, system memory 1410 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system1400, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 1410 also illustratesapplication programs 1412, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 1414, and an operating system 1416. By wayof example, operating system 1416 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, andPalm® OS operating systems.

Storage subsystem 1418 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some aspects. Software (programs, codemodules, instructions) that when executed by a processor provide thefunctionality described above, may be stored in storage subsystem 1418.These software modules or instructions may be executed by processingunit 1404. Storage subsystem 1418 may also provide a repository forstoring data used in accordance with the present invention.

Storage subsystem 1418 may also include a computer-readable storagemedia reader 1420 that can further be connected to computer-readablestorage media 1422. Together and, optionally, in combination with systemmemory 1410, computer-readable storage media 1422 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 1422 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media such as, but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible, non-transitorycomputer-readable storage media such as RAM, ROM, electronicallyerasable programmable ROM (EEPROM), flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD), or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible computer readablemedia. When specified, this can also include nontangible, transitorycomputer-readable media, such as data signals, data transmissions, orany other medium which can be used to transmit the desired informationand which can be accessed by computing system 1400.

By way of example, computer-readable storage media 1422 may include ahard disk drive that reads from or writes to non-removable, non-volatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, non-volatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, non-volatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 1422 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 1422 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 1400.

Communications subsystem 1424 provides an interface to other computersystems and networks. Communications subsystem 1424 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 1400. For example, communications subsystem 1424may enable computer system 1400 to connect to one or more devices viathe Internet. In some aspects, communications subsystem 1424 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 802.28 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some aspects, communications subsystem 1424 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some aspects, communications subsystem 1424 may also receive inputcommunication in the form of structured and/or unstructured data feeds1426, event streams 1428, event updates 1430, and the like on behalf ofone or more users who may use computer system 1400.

By way of example, communications subsystem 1424 may be configured toreceive unstructured data feeds 1426 in real-time from users of socialmedia networks and/or other communication services such as Twitter®feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS)feeds, and/or real-time updates from one or more third party informationsources.

Additionally, communications subsystem 1424 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 1428 of real-time events and/or event updates 1430, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 1424 may also be configured to output thestructured and/or unstructured data feeds 1426, event streams 1428,event updates 1430, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 1400.

Computer system 1400 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 1400 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious aspects.

In the foregoing specification, aspects of the invention are describedwith reference to specific aspects thereof, but those skilled in the artwill recognize that the invention is not limited thereto. Variousattributes and aspects of the above-described invention may be usedindividually or jointly. Further, aspects can be utilized in any numberof environments and applications beyond those described herein withoutdeparting from the broader spirit and scope of the specification. Thespecification and drawings are, accordingly, to be regarded asillustrative rather than restrictive.

What is claimed is:
 1. A method for providing conversationalexplanations, comprising: obtaining, by one or more processors, a textcomprising an explanation associated with a decision-making processrelated to a machine-learning model; identifying, by the one or moreprocessors from an explanation chain comprising an ordered chain ofpremises generated from the text, that the text comprising theexplanation is incomplete based at least in part on determining that afirst premise and a second premise of the explanation chain lack aparticular type of rhetorical relationship, the ordered chain ofpremises being generated based on generating a discourse tree by parsingthe text in accordance with rhetorical structure theory; obtaining, bythe one or more processors, additional text based at least in part onexecuting a query comprising at least a portion of the first premise;associating the text with an augmented explanation chain generated froma complete discourse tree, the complete discourse tree being generatedbased on combining the discourse tree generated from the text and anadditional discourse tree generated from the additional text; receiving,by the one or more processors, a user query; identifying, by the one ormore processors, a set of decision features that are associated with adecision generated by the machine-learning model; identifying, by theone or more processors, the text is related to the decision-makingprocess associated with the machine-learning model; obtaining, by theone or more processors, the augmented explanation chain associated withthe text; correlating a term of the augmented explanation chain with adecision feature of the set of decision features; and providing, by theone or more processors, a response to the user query, the response beinggenerated based at least in part on replacing the term of the augmentedexplanation chain with the decision feature, wherein the responseexplains the decision generated by the machine-learning model.
 2. Themethod of claim 1, further comprising: managing, by the one or moreprocessors, a decision log associated with the machine-learning model,the machine-learning model being trained to provide a correspondingdecision based at least in part on input data, the decision log storingsets of decision features of past decisions provided by themachine-learning model; and generating, by the one or more processors, aplurality of explanation chains comprising the explanation chain from acorpus of supporting documents based at least in part on generatingdiscourse trees from the corpus of supporting documents, wherein eachdiscourse tree comprises nodes and edges, the nodes comprising i)nonterminal nodes representing a corresponding rhetorical relationshipbetween two fragments of a document and i) terminal nodes representingthe fragments.
 3. The method of claim 2, wherein generating theplurality of explanation chains is performed as an offline process. 4.The method of claim 2, wherein generating the plurality of explanationchains further comprises: constructing, from the text, the discoursetree based at least in part on segmenting the text into elementarydiscourse units and identifying rhetorical relationships betweenrespective pairs of elementary discourse units in accordance withrhetorical structure theory; generating, from the elementary discourseunits of the discourse tree, the ordered chain of premises comprisingthe first premise of the text and the second premise of the text basedat least in part on identifying, from the discourse tree, the particulartype of rhetorical relationship exists between a first elementarydiscourse unit including the first premise and a second elementarydiscourse unit including the second premise; determining, from theordered chain of premises, that a first entity of the first premise anda second entity of the second premise of the ordered chain of premiseslack a logical connection; forming the query with an entity of the firstentity of the first premise; obtaining, based at least in part onexecuting a search with the query, a candidate search resultcorresponding to the query, the candidate search result comprising theadditional text, wherein generating the additional discourse treecomprises parsing the additional text in accordance with rhetoricalstructure theory; and responsive to determining that the additionaldiscourse tree includes the rhetorical relationship between the firstentity of the first premise and the second entity of the second premise,forming the complete discourse tree based on inserting the additionaldiscourse tree generated from the candidate search result as a sub-treeof the discourse tree generated from the text.
 5. The method of claim 4,further comprising indexing the decision log associated with themachine-learning model.
 6. The method of claim 1, further comprisingdetermining a classification for the user query, the classificationbeing selected from: i) a factoid question classification, ii) aspecific question classification, or iii) a transactional requestclassification; and determining that the user query relates to thespecific question based on determining that the classification for theuser query is the specific question classification.
 7. The method ofclaim 1, further comprising: receiving input data associated with auser; generating the decision based at least in part by providing theinput data to the machine-learning model, wherein classifying the inputdata causes the machine-learning model to generate the set of decisionfeatures associated with the decision; and associating the input datawith the set of decision features associated with the decision.
 8. Themethod of claim 1, wherein the explanation chain is generated from acorpus of supporting documents related to the decision-making processthe corpus of supporting documents comprising the text.
 9. A computingdevice comprising: a computer-readable medium storing non-transitorycomputer-executable program instructions; and a processing devicecommunicatively coupled to the computer-readable medium and configuredto execute the non-transitory computer-executable program instructions,wherein executing the non-transitory computer-executable programinstructions causes the processing device to perform operationscomprising: obtaining a text comprising an explanation associated with adecision-making process related to a machine-learning model;identifying, from an explanation chain comprising an ordered chain ofpremises generated from the text, that the text comprising theexplanation is incomplete based at least in part on determining that afirst premise and a second premise of the explanation chain lack aparticular type of rhetorical relationship, the ordered chain ofpremises being generated based on generating a discourse tree by parsingthe text in accordance with rhetorical structure theory; obtainingadditional text based at least in part on executing a query comprisingat least a portion of the first premise; associating the text with anaugmented explanation chain generated from a complete discourse tree,the complete discourse tree being generated based on combining thediscourse tree generated from the text and an additional discourse treegenerated from the additional text; receiving a user query; identifyinga set of decision features that are associated with a decision generatedby the machine-learning model; identifying the text as being related tothe user query and the decision-making process associated with themachine-learning model; obtaining the augmented explanation chainassociated with the text; correlating a term of the explanation chainwith a decision feature of the set of decision features; and providing aresponse to the user query, the response being generated based at leastin part on replacing the term of the augmented explanation chain withthe decision feature, wherein the response explains the decisiongenerated by the machine-learning model.
 10. The computing device ofclaim 9, wherein the processing device performs further operationscomprising: managing a decision log associated with the machine-learningmodel, the machine-learning model being trained to provide the decisionbased at least in part on input data, the decision log storing sets ofdecision features of past decisions provided by the machine-learningmodel; and generating a plurality of explanation chains comprising theexplanation chain from a corpus of supporting documents based at leastin part on generating discourse trees from the corpus of supportingdocuments, wherein each discourse tree comprises nodes and edges, thenodes comprising i) nonterminal nodes representing a correspondingrhetorical relationship between two fragments of a document and i)terminal nodes representing the fragments.
 11. The computing device ofclaim 10, wherein generating the plurality of explanation chains furthercomprises: constructing, from the text, the discourse tree based atleast in part on segmenting the text into elementary discourse units andidentifying rhetorical relationships between respective pairs ofelementary discourse units in accordance with rhetorical structuretheory; generating, from the elementary discourse units of the discoursetree, the ordered chain of premises comprising the first premise of thetext and the second premise of the text based at least in part onidentifying, from the discourse tree, the particular type of rhetoricalrelationship exists between a first elementary discourse unit includingthe first premise and a second elementary discourse unit including thesecond premise; determining, from the ordered chain of premises, that afirst entity of the first premise and a second entity of the secondpremise of the ordered chain of premises lack a logical connection;forming the query with an entity of the first entity of the firstpremise; obtaining, based at least in part on executing a search withthe query, a candidate search result corresponding to the query, thecandidate search result comprising the additional text, whereingenerating the additional discourse tree comprises parsing theadditional text in accordance with rhetorical structure theory; andresponsive to determining that the additional discourse tree includesthe rhetorical relationship between the first entity of the firstpremise and the second entity of the second premise, forming thecomplete discourse tree based on inserting the additional discourse treegenerated from the candidate search result as a sub-tree of thediscourse tree generated from the text.
 12. The computing device ofclaim 11, wherein the processing device performs further operationscomprising indexing the decision log associated with themachine-learning model.
 13. The computing device of claim 9, wherein theprocessing device performs further operations comprising: receivinginput data associated with a user; generating the decision based atleast in part by providing the input data to the machine-learning model,wherein classifying the input data causes the machine-learning model togenerate the set of decision features associated with the decision; andassociating the input data with the set of decision features associatedwith the decision.
 14. The computing device of claim 9, wherein theexplanation chain is generated from a corpus of supporting documentsrelated to the decision-making process the corpus of supportingdocuments comprising the text.
 15. A non-transitory computer-readablemedium storing computer-executable program instructions, wherein whenexecuted by a processing device, causes the processing device to performoperations comprising: obtaining a text comprising an explanationassociated with a decision-making process related to a machine-learningmodel; identifying, from an explanation chain comprising an orderedchain of premises generated from the text, that the text comprising theexplanation is incomplete based at least in part on determining that afirst premise and a second premise of the explanation chain lack aparticular type of rhetorical relationship, the ordered chain ofpremises being generated based on generating a discourse tree by parsingthe text in accordance with rhetorical structure theory; obtainingadditional text based at least in part on executing a query comprisingat least a portion of the first premise; associating the text with anaugmented explanation chain generated from a complete discourse tree,the complete discourse tree being generated based on combining thediscourse tree generated from the text and an additional discourse treegenerated from the additional text; receiving a user query; identifyinga set of decision features that are associated with a decision generatedby the machine-learning model; identifying the text as being related tothe user query and the decision-making process associated with themachine-learning model; obtaining the augmented explanation chainassociated with the text; correlating a term of the explanation chainwith a decision feature of the set of decision features; and providing aresponse to the user query, the response being generated based at leastin part on replacing the term of the augmented explanation chain withthe decision feature, wherein the response explains the decisiongenerated by the machine-learning model.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the processing deviceperforms further operations comprising: managing a decision logassociated with the machine-learning model, the machine-learning modelbeing trained to provide the decision based at least in part on inputdata, the decision log storing sets of decision features of thedecisions provided by the machine-learning model; and generating aplurality of explanation chains comprising the explanation chain from acorpus of supporting documents based at least in part on generatingdiscourse trees from the corpus of supporting documents, wherein eachdiscourse tree comprises nodes and edges, the nodes comprising i)nonterminal nodes representing a corresponding rhetorical relationshipbetween two fragments of a document and i) terminal nodes representingthe fragments.
 17. The non-transitory computer-readable medium of claim16, wherein generating the plurality of explanation chains furthercomprises: constructing, from the text, the discourse tree based atleast in part on segmenting the text into elementary discourse units andidentifying rhetorical relationships between respective pairs ofelementary discourse units in accordance with rhetorical structuretheory; generating, from the elementary discourse units of the discoursetree, the ordered chain of premises comprising the first premise of thetext and the second premise of the text based at least in part onidentifying, from the discourse tree, the particular type of rhetoricalrelationship exists between a first elementary discourse unit includingthe first premise and a second elementary discourse unit including thesecond premise; determining, from the ordered chain of premises, that afirst entity of the first premise and a second entity of the secondpremise of the ordered chain of premises lack a logical connection;forming the query with an entity of the first entity of the firstpremise; obtaining, based at least in part on executing a search withthe query, a candidate search result corresponding to the query, thecandidate search result comprising the additional text, whereingenerating the additional discourse tree comprises parsing theadditional text in accordance with rhetorical structure theory; andresponsive to determining that the additional discourse tree includesthe rhetorical relationship between the first entity of the firstpremise and the second entity of the second premise, forming thecomplete discourse tree based on inserting the additional discourse treegenerated from the candidate search result as a sub-tree of thediscourse tree generated from the text.
 18. The non-transitorycomputer-readable medium of claim 17, wherein the processing deviceperforms further operations comprising indexing the decision logassociated with the machine-learning model.
 19. The non-transitorycomputer-readable medium of claim 15, wherein the processing deviceperforms further operations comprising: receiving input data associatedwith a user; generating the decision based at least in part by providingthe input data to the machine-learning model, wherein classifying theinput data causes the machine-learning model to generate the set ofdecision features associated with the decision; and associating theinput data with the set of decision features associated with thedecision.
 20. The non-transitory computer-readable medium of claim 15,wherein the explanation chain is generated from a corpus of supportingdocuments related to the decision-making process the corpus ofsupporting documents comprising the text.